14 Free Agriculture Datasets for Machine Learning

Article by Alex Nguyen | January 29, 2019

Artificial intelligence has created opportunities across many major industries, and agriculture is no exception. Applying machine learning technologies to traditional agricultural systems can lead to faster, more accurate decision making for farmers and policy makers alike. As the foundation of many world economies, the agricultural industry is ripe with public data to use for machine learning.

We at Lionbridge AI have gathered the best publicly available agricultural datasets for machine learning projects:

 

Agriculture Datasets for Machine Learning

USDA Datamart: USDA pricing data on livestock, poultry, and grain. Contains complete unrestricted public access to aggregated data sets for Livestock Mandatory Reporting (LMR) data and Dairy Mandatory Price Reporting (DMPR) Programs since 2010.

Global Food & Agriculture Statistics: Access to over 3 million time-series and cross sectional data relating to food and agriculture. Contains data for 200 countries and more than 200 primary products and inputs.

Daily Vegetable and Fruits Prices data 2010-2018: This data set is having historical prices of Fruits and vegetables in Bengaluru, India from 2010-2018.

Agriculture Crop Production In India: Describes the Agriculture Crops Cultivation/Production in India from 2001-2014.

China Agro. & Econ. Data: The China agricultural and economic database is a collection of agricultural-related data from official statistical publications of the People’s Republic of China.

Worldwide foodfeed production and distribution: Contains food and agriculture data for over 245 countries and territories, from 1961-2013. This dataset provides an insight on our worldwide food production – focusing on a comparison between food produced for human consumption and feed produced for animals.

The National Summary of Meats: Released by the US Department of Agriculture, this dataset contains records on meat production and quality as far back as 1930.

Corn & Soybean Prices 2008-2017: Prices with USDA WASDE Monthly Projections for various U.S. crops. The USDA data was acquired by downloading all the historical WASDE reports starting from 2008-2018.

Pesticide Use in Agriculture: This dataset includes annual county-level pesticide use estimates for 423 pesticides (active ingredients) applied to agricultural crops grown in the contiguous United States.

Agricultural Land Values (1997-2017): The National Agricultural Statics Service (NASS) publishes data about varying aspects of the agricultural industry. Since 1997, the service has compiled data regarding the value per acre of farmland in each state/region in the United States.

V2 Plant Seedlings Dataset: A dataset of 5,539 images of crop and weed seedlings belonging to 12 species. Each class contains rgb images that show plants at different growth stages. The images are in various sizes and are in png format.

 

Food Environment Atlas 2018: A dataset containing over 275 variables for researchers to study the interaction of access to healthy food options, demographic factors and economic indicators to inform policymakers.

Feed Grains Database: Statistics on four feed grains (corn, grain sorghum, barley, and oats), foreign coarse grains, hay, and related items

Fertilizer Use and Price: Data on fertilizer consumption in the United States from 1960-2012 by plant nutrient and major selected product, as well as consumption of mixed fertilizers, secondary nutrients, and micronutrients.

 

In case you missed our previous dataset compilations, you can find them all here. Still can’t find the custom data you need to train your model? Lionbridge AI provides custom AI training data in 300 languages for your specific machine learning project needs.

Contact us to learn more about how Lionbridge AI can work for you.

The Author
Alex Nguyen

Alex manages content production for Lionbridge’s marketing team. Originally from San Francisco but based in Tokyo, she loves all things culture and design. When not at Lionbridge, she’s likely brushing up on her Japanese, letting loose at indie electronic shows or trying out new ice cream spots in the city.

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